The present invention relates to data compression and decompression, and more specifically, to methods and apparatuses for compressing and decompressing signal data.
In the field of wireless signal processing, e.g., in a base transceiver system or an information acquisition and processing system, a signal is typically modulated into two orthogonal data branches, i.e. I and Q data branches, and in a digital signal processor, the two data branches I and Q are usually represented by fixed-length fixed-point numbers (taking a 16-bit analog-to-digital converter as an example, if an analog signal is inputted, outputted two data branches I and Q are both 16-bit binary fixed-point numbers). These I/Q data have the following characteristics:
(1) A relatively larger set of data. Taking 16-bit I/Q data as an example, a size of the data set is 216.
(2) Relatively low percentage of occurrences frequency of a single numeric value, which is usually lower than 1%.
(3) Close value ranges of consecutive data. Taking 16-bit I/Q data as an example, several consecutive data might fall within a range of [26, 27).
Compression of I/Q signal data with the foregoing characteristics enables more efficient use of resources. Specifically, in a base transceiver system, a compressor performs compression to signal data whereby the amount of signal data in transmission links can be reduced and thus the bandwidth can be saved; in an information acquisition and processing system, a compressor performs compression to signal data whereby the amount of signal data to be stored can be reduced and thus the capacity of storage devices can be saved.
However, traditional data compression methods based on information entropy theory, either statistics-based Huffman coding and arithmetic coding, or dictionary-based compression methods (like LZW), are far from satisfactory in terms of compression complexity, decompression complexity and compression efficiency. “A relatively larger set of data” means maintaining and storing a relatively larger table, which makes the compression complexity and decompression complexity relatively high; “relatively low percentage of occurrences frequency of a single numeric value” implies relatively bad compression efficiency.
Therefore, there is a need for a high-efficiency compression method for signal data.